A wavelet-based algorithm without a priori knowledge of noise level for gross errors detection
Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review
Authors
This paper deals with Gross Error Detection using a signal-based approach and proposes an algorithm to be applied in industrial processes. The developed algorithm is used in some industrial software platforms to detect sensor outliers. A validation of this algorithm through computer simulations is shown. At the end of the paper, results using real sensor measurements from industrial processes are presented.
| Original language | English |
|---|---|
| Title of host publication | Advances in Intelligent Systems |
| Editors | Ford Lumban Gaol, Zenon Chaczko, Kiyota Hashimoto, Tokoro Matsuo, William Grosky |
| Number of pages | 8 |
| Place of Publication | Southampton (UK) |
| Publisher | WIT Press |
| Publication date | 2014 |
| Pages | 9-16 |
| ISBN (print) | 978-184564869-5 |
| ISBN (electronic) | 978-1-84564-870-1 |
| DOIs | |
| Publication status | Published - 2014 |
Bibliographical note
Extended paper from the International Conference on Advances in Intelligent Systems in Bioinformatics (2013), Atlantis Press.
- Engineering - Fault detection, Industrial applications, Wavelets